Traffic Agents Trajectory Prediction Based on Enhanced Bidirectional Recurrent Network and Adaptive Social Interaction Model

Accurate prediction of the future trajectory of traffic agents is imperative to the effective motion planning of autonomous vehicles and mobile robots. Despite enormous progress that has been made toward trajectory prediction, dynamic and crowded traffic scenarios pose major challenges to the understanding and forecasting of traffic agents’ motion behavior. In this paper, we propose a novel trajectory prediction method from the perspective of temporal modeling and social interaction. Specifically, we first put forward a recurrent modeling approach to learn temporal features in favor of capturing long-range and short-range temporal dependencies of individual agents. Then, we construct a social feature learning module to capture the sparse and directional interactions among agents while suppressing the spurious connections. Finally, to reduce the accumulated error during prediction, a coordinated bidirectional decoding module is developed where temporal and social features can be properly integrated into the forward and backward prediction processes. Extensive experiments are performed on four real-world trajectory prediction benchmarks, and the results demonstrate the superiority of our method compared with other competing approaches. Detailed ablation studies are also performed to evaluate the effectiveness of each model component. Note to Practitioners—Motion planning is one of the crucial components of autonomous systems, such as intelligent vehicles and mobile robots. For example, the safety and efficiency of motion planning can be drastically improved if the future trajectories of surrounding agents, e.g., pedestrians, bicyclists, cars, etc., can be accurately forecasted. Motivated by the above requirements, this article develops an advanced deep learning model that can learn temporal and social features from trajectory data and perform accuracy prediction. This work aims to enhance the bidirectional recurrent network for dealing with trajectory data with evident temporal characteristics. In addition, this work introduces a novel adaptive social interaction modeling approach that overcomes the inherent defect of the fixed threshold method. This work also addresses the error accumulation problem in the prediction. The proposed model is evaluated on several datasets, and the results demonstrate its effectiveness. Our approach has broad application prospects in autonomous driving and mobile robots.